Computational Growth and Remodeling of Abdominal Aortic Aneurysms Constrained by the Spine

Variability in structure, morphology, and mechanical properties of the descending thoracic and infrarenal aorta around their circumference

Aortic diseases, such as aneurysms, atherosclerosis, and dissections, demonstrate a preferential development and progression around the aortic circumference, resulting in a highly heterogeneous disease state around the circumference. Differences in the aorta's structural composition and mechanical properties may be partly responsible for this phenomenon. Our goal in this study was to analyze the mechanical and structural properties of the human aorta at its lateral, anterior, posterior, and medial quadrants in two regions prone to circumferentially inhomogeneous diseases, descending Thoracic Aorta (TA) and Infrarenal Aorta (IFR). Human aortas were obtained from 10 donors (64 ± 11 years) and dissected from their loose surrounding tissue. Mechanical properties were determined in all four quadrants of TA and IFR using planar biaxial testing and fitted to three common constitutive models. The structure of tissues was assessed using Movat Pentachrome stained histology slides. We observed that the anterior quadrant exhibited the greatest thickness, followed by the lateral region, in both the TA and IFR. In TA, the posterior wall appeared as the stiffest location in most samples, while in IFR, the anterior wall was the stiffest. We observed a higher glycosaminoglycans content in the lateral and posterior regions of the IFR. We found elastin density to be similar in TA lateral, anterior, and posterior quadrants, while in IFR, the anterior region demonstrated the highest elastin density. Despite significant variations between subjects, this study highlights the distinct morphometrical, mechanical, and structural properties between the quadrants of both TA and IFR.

Stress Analysis in AAA does not Predict Rupture Location Correctly in Patients with Intraluminal Thrombus

BACKGROUND

A biomechanical approach to the rupture risk of an abdominal aortic aneurysm could be a solution to ensure a personalized estimate of this risk. It is still difficult to know in what conditions, the assumptions made by biomechanics, are valid. The objective of this work was to determine the individual biomechanical rupture threshold and to assess the correlation between their rupture sites and the locations of their maximum stress comparing two computed tomography scan (CT) before and at time of rupture.

METHODS

We included 5 patients who had undergone two CT; one within the last 6 months period before rupture and a second CT scan just before the surgical procedure for the rupture. All DICOM data, both pre- and rupture, were processed following the same following steps: generation of a 3D geometry of the abdominal aortic aneurysm, meshing and computational stress analysis using the finite element method. We used two different modelling scenarios to study the distribution of the stresses, a "wall" model without intraluminal thrombus (ILT) and a "thrombus" model with ILT.

RESULTS

The average time between the pre-rupture and rupture CT scans was 44 days (22-97). The median of the maximum stresses applied to the wall between the pre-rupture and rupture states were 0.817 MPa (0.555-1.295) and 1.160 MPa (0.633-1.625) for the "wall" model; and 0.365 MPa (0.291-0.753) and 0.390 MPa (0.343-0.819) for the "thrombus" model. There was an agreement between the site of rupture and the location of maximum stress for only 1 patient, who was the only patient without ILT.

CONCLUSIONS

We observed a large variability of stress values at rupture sites between patients. The rupture threshold strongly varied between individuals depending on the intraluminal thrombus. The site of rupture did not correlate with the maximum stress except for 1 patient.

Vascular Deformation Mapping of Abdominal Aortic Aneurysm

Abdominal aortic aneurysm (AAA) is a complex disease that requires regular imaging surveillance to monitor for aneurysm stability. Current imaging surveillance techniques use maximum diameter, often assessed by computed tomography angiography (CTA), to assess risk of rupture and determine candidacy for operative repair. However, maximum diameter measurements can be variable, do not reliably predict rupture risk and future AAA growth, and may be an oversimplification of complex AAA anatomy. Vascular deformation mapping (VDM) is a recently described technique that uses deformable image registration to quantify three-dimensional changes in aortic wall geometry, which has been previously used to quantify three-dimensional (3D) growth in thoracic aortic aneurysms, but the feasibility of the VDM technique for measuring 3D growth in AAA has not yet been studied. Seven patients with infra-renal AAAs were identified and VDM was used to identify three-dimensional maps of AAA growth. In the present study, we demonstrate that VDM is able to successfully identify and quantify 3D growth (and the lack thereof) in AAAs that is not apparent from maximum diameter. Furthermore, VDM can be used to quantify growth of the excluded aneurysm sac after endovascular aneurysm repair (EVAR). VDM may be a useful adjunct for surgical planning and appears to be a sensitive modality for detecting regional growth of AAAs.

Imaging Predictive Factors of Abdominal Aortic Aneurysm Growth

Background: Variable imaging methods may add important information about abdominal aortic aneurysm (AAA) progression. The aim of this study is to assess available literature data regarding the predictive imaging factors of AAA growth. Methods: This systematic review was conducted using the PRISMA guidelines. A review of the literature was conducted, using PubMed, EMBASE and CENTRAL databases. The quality of the studies was assessed using the Newcastle-Ottawa Scale. Primary outcomes were defined as AAA growth rate and factors associated to sac expansion. Results: The analysis included 23 studies. All patients (2244; mean age; 69.8 years, males; 85%) underwent imaging with different modalities; the initial evaluation was followed by one or more studies to assess aortic expansion. AAA initial diameter was reported in 13 studies (range 19.9–50.9 mm). Mean follow-up was 34.5 months. AAA diameter at the end was ranging between 20.3 and 55 mm. The initial diameter and intraluminal thrombus were characterized as prognostic factors associated to aneurysm expansion. A negative association between atherosclerosis and AAA expansion was documented. Conclusions: Aneurysm diameter is the most studied factor to be associated with expansion and the main indication for intervention. Appropriate diagnostic modalities may account for different anatomical characteristics and identify aneurysms with rapid growth and higher rupture risk. Future perspectives, including computed mathematical models that will assess wall stress and elasticity and further flow characteristics, may offer valuable alternatives in AAA growth prediction.

Mechanical characterization of abdominal aortas using multi-perspective ultrasound imaging

Mechanical characterization of abdominal aortic aneurysms using personalized biomechanical models is being widely investigated as an alternative criterion to assess risk of rupture. These methods rely on accurate wall motion detection and appropriate model boundary conditions. In this study, multi-perspective ultrasound is combined with finite element models to perform mechanical characterization of abdominal aortas in volunteers. Multi-perspective biplane radio frequency ultrasound recordings were made under seven angles (-45° to 45°) in one phantom set-up and eight volunteers, which were merged using automatic image registration. 2-D displacement fields were estimated in the seven longitudinal ultrasound views, creating a sparse, high resolution 3-D map of the wall motion at relatively high frame rates (20-27 Hz). The displacements were used to personalize the subject-specific finite element model of which the geometry of the aorta, spine, and surrounding tissue were determined from a single 3-D ultrasound acquisition. Automatic registration of the multi-perspective images was successful in six out of eight cases with an average error of 5.4° compared to the ground truth. Displacements of the aortic wall were measured and cyclic strain of the aortic diameter was found ranging from 4.2% to 8.6%. The subject-specific mesh and inverse FE analysis was performed yielding shear moduli estimates for the wall between 104 and 215 kPa. Comparative results from a single-perspective workflow revealed very low aortic wall motion signal, which resulted in relatively high modulus estimates, between 230 and 754 kPa. Multi-perspective biplane ultrasound imaging was used to personalize finite element models of the abdominal aorta and its surroundings, and performing mechanical characterization of the aortic shear modulus. The method was found to be a more robust method compared to a single-perspective 3-D ultrasound approach. Future research will focus on investigating the use of multiple 3-D ultrasound acquisitions, the feasibility of free-hand scanning, the creation of a full 3-D automatic registration process, and with that, enable a clinical continuation of this study.

Multiperspective Ultrasound Strain Imaging of the Abdominal Aorta

Current decision-making for clinical intervention of abdominal aortic aneurysms (AAAs) is based on the maximum diameter of the aortic wall, but this does not provide patient-specific information on rupture risk. Ultrasound (US) imaging can assess both geometry and deformation of the aortic wall. However, low lateral contrast and resolution are currently limiting the precision of both geometry and local strain estimates. To tackle these drawbacks, a multiperspective scanning mode was developed on a dual transducer US system to perform strain imaging at high frame rates. Experimental imaging was performed on porcine aortas embedded in a phantom of the abdomen, pressurized in a mock circulation loop. US images were acquired with three acquisition schemes: Multiperspective ultrafast imaging, single perspective ultrafast imaging, and conventional line-by-line scanning. Image registration was performed by automatic detection of the transducer surfaces. Multiperspective images and axial displacements were compounded for improved segmentation and tracking of the aortic wall, respectively. Performance was compared in terms of image quality, motion tracking, and strain estimation. Multiperspective compound displacement estimation reduced the mean motion tracking error over one cardiac cycle by a factor 10 compared to conventional scanning. Resolution increased in radial and circumferential strain images, and circumferential signal-to-noise ratio (SNRe) increased by 10 dB. Radial SNRe is high in wall regions moving towards the transducer. In other regions, radial strain estimates remain cumbersome for the frequency used. In conclusion, multiperspective US imaging was demonstrated to improve motion tracking and circumferential strain estimation of porcine aortas in an experimental set-up.

Predicting abdominal aortic aneurysm growth using patient-oriented growth models with two-step Bayesian inference

OBJECTIVE

For small abdominal aortic aneurysms (AAAs), a regular follow-up examination is recommended every 12 months for AAAs of 30-39 mm and every six months for AAAs of 40-55 mm. Follow-up diameters can determine if a patient follows the common growth model of the population. However, the rapid expansion of an AAA, often associated with higher rupture risk, may be overlooked even though it requires surgical intervention. Therefore, the prognosis of abdominal aortic aneurysm growth is clinically important for planning treatment. This study aims to build enhanced Bayesian inference methods to predict maximum aneurysm diameter.

METHODS

106 CT scans from 25 Korean AAA patients were retrospectively obtained. A two-step approach based on Bayesian calibration was used, and an exponential abdominal aortic aneurysm growth model (population-based) was specified according to each individual patient's growth (patient-specific) and morphologic characteristics of the aneurysm sac (enhanced). The distribution estimates were obtained using a Markov Chain Monte Carlo (MCMC) sampler.

RESULTS

The follow-up diameters were predicted satisfactorily (i.e. the true follow-up diameter was in the 95% prediction interval) for 79% of the scans using the population-based growth model, and 83% of the scans using the patient-specific growth model. Among the evaluated geometric measurements, centerline tortuosity was a significant (p = 0.0002) predictor of growth for AAAs with accelerated and stable expansion rates. Using the enhanced prediction model, 86% of follow-up scans were predicted satisfactorily. The average prediction errors of population-based, patient-specific, and enhanced models were ±2.67, ±2.61 and ± 2.79 mm, respectively.

CONCLUSION

A computational framework using patient-oriented growth models provides useful tools for per-patient basis treatment and enables better prediction of AAA growth.

Patient-Specific Prediction of Abdominal Aortic Aneurysm Expansion Using Bayesian Calibration

Translating recent advances in abdominal aortic aneurysm (AAA) growth and remodeling (G&R) knowledge into a predictive, patient-specific clinical treatment tool requires a major paradigm shift in computational modeling. The objectives of this study are to develop a prediction framework that first calibrates the physical AAA G&R model using patient-specific serial computed tomography (CT) scan images, predicts the expansion of an AAA in the future, and quantifies the associated uncertainty in the prediction. We adopt a Bayesian calibration method to calibrate parameters in the G&R computational model and predict the magnitude of AAA expansion. The proposed Bayesian approach can take different sources of uncertainty; therefore, it is well suited to achieve our aims in predicting the AAA expansion process as well as in computing the propagated uncertainty. We demonstrate how to achieve the proposed aims by solving the formulated Bayesian calibration problems for cases with the synthetic G&R model output data and real medical patient-specific CT data. We compare and discuss the performance of predictions and computation time under different sampling cases of the model output data and patient data, both of which are simulated by the G&R computation. Furthermore, we apply our Bayesian calibration to real patient-specific serial CT data and validate our prediction. The accuracy and efficiency of the proposed method is promising, which appeals to computational and medical communities.

Assessment of the regional distribution of normalized circumferential strain in the thoracic and abdominal aorta using DENSE cardiovascular magnetic resonance

BACKGROUND

Displacement Encoding with Stimulated Echoes (DENSE) cardiovascular magnetic resonance (CMR) of the aortic wall offers the potential to improve patient-specific diagnostics and prognostics of diverse aortopathies by quantifying regionally heterogeneous aortic wall strain in vivo. However, before regional mapping of strain can be used to clinically assess aortic pathology, an evaluation of the natural variation of normal regional aortic kinematics is required.

METHOD

Aortic spiral cine DENSE CMR was performed at 3 T in 30 healthy adult subjects (range 18 to 65 years) at one or more axial locations that are at high risk for aortic aneurysm or dissection: the infrarenal abdominal aorta (IAA, n = 11), mid-descending thoracic aorta (DTA, n = 17), and/or distal aortic arch (DAA, n = 11). After implementing custom noise-reduction techniques, regional circumferential Green strain of the aortic wall was calculated across 16 sectors around the aortic circumference at each location and normalized by the mean circumferential strain for comparison between individuals.

RESULTS

The distribution of normalized circumferential strain (NCS) was heterogeneous for all locations evaluated. Despite large differences in mean strain between subjects, comparisons of NCS revealed consistent patterns of strain distribution for similar groupings of patients by axial location, age, and/or mean displacement angle. NCS at local systole was greatest in the lateral/posterolateral walls in the IAAs (1.47 ± 0.27), medial wall in anteriorly displacing DTAs (1.28 ± 0.20), lateral wall in posteriorly displacing DTAs (1.29 ± 0.29), superior curvature in DAAs < 50 years-old (1.93 ± 0.22), and medial wall in DAAs > 50 years (2.29 ± 0.58). The distribution of strain was strongly influenced by the location of the vertebra and other surrounding structures unique to each location.

CONCLUSIONS

Regional in vivo circumferential strain in the adult aorta is unique to each axial location and heterogeneous around its circumference, but can be grouped into consistent patterns defined by basic patient-specific metrics following normalization. The heterogeneous strain distributions unique to each group may be due to local peri-aortic constraints (particularly at the aorto-vertebral interface), heterogeneous material properties, and/or heterogeneous flow patterns. These results must be carefully considered in future studies seeking to clinically interpret or computationally model patient-specific aortic kinematics.

Manufacturing Abdominal Aorta Hydrogel Tissue-Mimicking Phantoms for Ultrasound Elastography Validation

Ultrasound (US) elastography, or elasticity imaging, is an adjunct imaging technique that utilizes sequential US images of soft tissues to measure the tissue motion and infer or quantify the underlying biomechanical characteristics. For abdominal aortic aneurysms (AAA), biomechanical properties such as changes in the tissue's elastic modulus and estimates of the tissue stress may be essential for assessing the need for the surgical intervention. Abdominal aortic aneurysms US elastography could be a useful tool to monitor AAA progression and identify changes in biomechanical properties characteristic of high-risk patients. A preliminary goal in the development of an AAA US elastography technique is the validation of the method using a physically relevant model with known material properties. Here we present a process for the production of AAA tissue-mimicking phantoms with physically relevant geometries and spatially modulated material properties. These tissue phantoms aim to mimic the US properties, material modulus, and geometry of the abdominal aortic aneurysms. Tissue phantoms are made using a polyvinyl alcohol cryogel (PVA-c) and molded using 3D printed parts created using computer aided design (CAD) software. The modulus of the phantoms is controlled by altering the concentration of PVA-c and by changing the number of freeze-thaw cycles used to polymerize the cryogel. The AAA phantoms are connected to a hemodynamic pump, designed to deform the phantoms with the physiologic cyclic pressure and flows. Ultra sound image sequences of the deforming phantoms allowed for the spatial calculation of the pressure normalized strain and the identification of mechanical properties of the vessel wall. Representative results of the pressure normalized strain are presented.

In vivo quantification of regional circumferential Green strain in the thoracic and abdominal aorta by 2D spiral cine DENSE MRI

INTRODUCTION

Regional tissue mechanics play a fundamental role in patient-specific cardiovascular function. Nevertheless, regional assessments of aortic kinematics remain lacking due to the challenge of imaging the thin aortic wall. Herein, we present a novel application of DENSE (Displacement Encoding with Stimulated Echoes) MRI to quantify the circumferential Green strain of the thoracic and abdominal aorta.

METHODS

2D spiral cine DENSE and steady-state free procession (SSFP) cine images were acquired at 3T at the infrarenal aorta (IAA), descending thoracic aorta (DTA), or distal aortic arch (DAA) in a pilot study of 6 healthy volunteers. DENSE data was processed with multiple custom noise-reduction techniques to calculate circumferential Green strain across 16 equispaced sectors around the aorta. Each volunteer was scanned twice to evaluate interstudy repeatability.

RESULTS

Circumferential strain was heterogeneously distributed in all volunteers and locations. Spatial heterogeneity index by location was 0.37 (IAA), 0.28 (DTA), and 0.59 (DAA). Mean peak strain by DENSE for each cross-section was consistent with the homogenized linearized strain estimated from SSFP cine. The mean difference in peak strain across all sectors following repeat imaging was -0.1±2.2%, with a mean absolute difference of 1.7%.

CONCLUSIONS

Aortic cine DENSE MRI is a viable non-invasive technique for quantifying heterogeneous regional aortic wall strain and has significant potential to improve patient-specific clinical assessments of numerous aortopathies, as well as to provide the lacking spatiotemporal data required to refine computational models of aortic growth and remodeling.

Prediction of Abdominal Aortic Aneurysm Growth Using Dynamical Gaussian Process Implicit Surface

OBJECTIVE

We propose a novel approach to predict the Abdominal Aortic Aneurysm (AAA) growth in future time, using longitudinal computer tomography (CT) scans of AAAs that are captured at different times in a patient-specific way.

METHODS

We adopt a formulation that considers a surface of the AAA as a manifold embedded in a scalar field over the three dimensional (3D) space. For this formulation, we develop our Dynamical Gaussian Process Implicit Surface (DGPIS) model based on observed surfaces of 3D AAAs as visible variables while the scalar fields are hidden. In particular, we use Gaussian process regression to construct the field as an observation model from CT training image data. We then learn a dynamic model to represent the evolution of the field. Finally, we derive the predicted AAA surface from the predicted field along with uncertainty quantified in future time.

RESULTS

A dataset of 7 subjects (4-7 scans) was collected and used to evaluate the proposed method by comparing its prediction Hausdorff distance errors against those of simple extrapolation. In addition, we evaluate the prediction results with respect to a conventional shape analysis technique such as Principal Component Analysis (PCA). All comparative results show the superior prediction performance of the proposed approach.

CONCLUSION

We introduce a novel approach to predict the AAA growth and its predicted uncertainty in future time, using longitudinal CT scans in a patient-specific fashion.

SIGNIFICANCE

The capability to predict the AAA shape and its confidence region by our approach establish the potential for guiding clinicians with informed decision in conducting medical treatment and monitoring of AAAs.

Detecting Regional Stiffness Changes in Aortic Aneurysmal Geometries Using Pressure-Normalized Strain

Transabdominal ultrasound elasticity imaging could improve the assessment of rupture risk for abdominal aortic aneurysms by providing information on the mechanical properties and stress or strain states of vessel walls. We implemented a non-rigid image registration method to visualize the pressure-normalized strain within vascular tissues and adapted it to measure total strain over an entire cardiac cycle. We validated the algorithm's performance with both simulated ultrasound images with known principal strains and anatomically accurate heterogeneous polyvinyl alcohol cryogel vessel phantoms. Patient images of abdominal aortic aneurysm were also used to illustrate the clinical feasibility of our imaging algorithm and the potential value of pressure-normalized strain as a clinical metric. Our results indicated that pressure-normalized strain could be used to identify spatial variations in vessel tissue stiffness. The results of this investigation were sufficiently encouraging to warrant a clinical study measuring abdominal aortic pressure-normalized strain in a patient population with aneurysmal disease.

Empirically Determined Vascular Smooth Muscle Cell Mechano-Adaptation Law

Cardiovascular disease can alter the mechanical environment of the vascular system, leading to mechano-adaptive growth and remodeling. Predictive models of arterial mechano-adaptation could improve patient treatments and outcomes in cardiovascular disease. Vessel-scale mechano-adaptation includes remodeling of both the cells and extracellular matrix. Here, we aimed to experimentally measure and characterize a phenomenological mechano-adaptation law for vascular smooth muscle cells (VSMCs) within an artery. To do this, we developed a highly controlled and reproducible system for applying a chronic step-change in strain to individual VSMCs with in vivo like architecture and tracked the temporal cellular stress evolution. We found that a simple linear growth law was able to capture the dynamic stress evolution of VSMCs in response to this mechanical perturbation. These results provide an initial framework for development of clinically relevant models of vascular remodeling that include VSMC adaptation.

Interaction of expanding abdominal aortic aneurysm with surrounding tissue: Retrospective CT image studies

OBJECTIVES

Abdominal aortic aneurysms (AAA) that rupture have a high mortality rate. Rupture occurs when local mechanical stress exceeds the local mechanical strength of an AAA, so stress profiles such as those from finite element analysis (FEA) are useful. The role and effect of surrounding tissues, like the vertebral column, which have not been extensively studied, are examined in this paper.

METHODS

Longitudinal CT scans from ten patients with AAAs were studied to see the effect of surrounding tissues on AAAs. Segmentation was performed to distinguish the AAA from other tissues and we studied how these surrounding tissues affected the shape and curvature of the AAA. Previously established methods by Veldenz et al. were used to split the AAA into 8 sections and examine the specific effects of surrounding tissues on these sections [1]. Three-dimensional models were created to better examine these effects over time. Registration was done in order to compare AAAs longitudinally.

RESULTS

The vertebral column and osteophytes were observed to have been affecting the shape and the curvature of the AAA. Interaction with the spine caused focal flattening in certain areas of the AAA. In 16 of the 41 CT scans, the right posterior dorsal section (section 5), had the highest radius of curvature, which was by far the section that had the maximum radius for a specified CT scan. Evolution of the growing AAA showed increased flattening in this section when comparing the last CT scan to the first scan.

CONCLUSION

Surrounding tissues have a clear influence on the geometry of an AAA, which may in turn affect the stress profile of AAA. Incorporating these structures in FEA and G&R models will provide a better estimate of stress.

CLINICAL RELEVANCE

Currently, size is the only variable considered when deciding whether to undergo elective surgery to repair AAA since it is an easy enough measure for clinicians to utilize. However, this may not be the best indicator of rupture risk because small aneurysms also contribute to a high mortality rate. AAA's wall stress is a superior indicator and may be better predicted with the inclusion of these surrounding tissues, which then could be used by clinicians in their decision-making process on whether to operate on an AAA.